from contextlib import contextmanager

import pytorch_lightning as pl
import torch
import torch.nn.functional as F
from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer

from modelscope.models.cv.image_to_3d.ldm.modules.diffusionmodules.model import (
    Decoder, Encoder)
from modelscope.models.cv.image_to_3d.ldm.modules.distributions.distributions import \
    DiagonalGaussianDistribution
from modelscope.models.cv.image_to_3d.ldm.util import instantiate_from_config


class VQModel(pl.LightningModule):

    def __init__(
            self,
            ddconfig,
            lossconfig,
            n_embed,
            embed_dim,
            ckpt_path=None,
            ignore_keys=[],
            image_key='image',
            colorize_nlabels=None,
            monitor=None,
            batch_resize_range=None,
            scheduler_config=None,
            lr_g_factor=1.0,
            remap=None,
            sane_index_shape=False,  # tell vector quantizer to return indices as bhw
            use_ema=False):
        super().__init__()
        self.embed_dim = embed_dim
        self.n_embed = n_embed
        self.image_key = image_key
        self.encoder = Encoder(**ddconfig)
        self.decoder = Decoder(**ddconfig)
        self.loss = instantiate_from_config(lossconfig)
        self.quantize = VectorQuantizer(
            n_embed,
            embed_dim,
            beta=0.25,
            remap=remap,
            sane_index_shape=sane_index_shape)
        self.quant_conv = torch.nn.Conv2d(ddconfig['z_channels'], embed_dim, 1)
        self.post_quant_conv = torch.nn.Conv2d(embed_dim,
                                               ddconfig['z_channels'], 1)
        if colorize_nlabels is not None:
            assert type(colorize_nlabels) == int
            self.register_buffer('colorize',
                                 torch.randn(3, colorize_nlabels, 1, 1))
        if monitor is not None:
            self.monitor = monitor
        self.batch_resize_range = batch_resize_range
        if self.batch_resize_range is not None:
            print(
                f'{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.'
            )

        self.use_ema = use_ema
        if self.use_ema:
            self.model_ema = LitEma(self)
            print(f'Keeping EMAs of {len(list(self.model_ema.buffers()))}.')

        if ckpt_path is not None:
            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
        self.scheduler_config = scheduler_config
        self.lr_g_factor = lr_g_factor

    @contextmanager
    def ema_scope(self, context=None):
        if self.use_ema:
            self.model_ema.store(self.parameters())
            self.model_ema.copy_to(self)
            if context is not None:
                print(f'{context}: Switched to EMA weights')
        try:
            yield None
        finally:
            if self.use_ema:
                self.model_ema.restore(self.parameters())
                if context is not None:
                    print(f'{context}: Restored training weights')

    def init_from_ckpt(self, path, ignore_keys=list()):
        sd = torch.load(
            path, map_location='cpu', weights_only=True)['state_dict']
        keys = list(sd.keys())
        for k in keys:
            for ik in ignore_keys:
                if k.startswith(ik):
                    print('Deleting key {} from state_dict.'.format(k))
                    del sd[k]
        missing, unexpected = self.load_state_dict(sd, strict=False)
        print(
            f'Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys'
        )
        if len(missing) > 0:
            print(f'Missing Keys: {missing}')
            print(f'Unexpected Keys: {unexpected}')

    def on_train_batch_end(self, *args, **kwargs):
        if self.use_ema:
            self.model_ema(self)

    def encode(self, x):
        h = self.encoder(x)
        h = self.quant_conv(h)
        quant, emb_loss, info = self.quantize(h)
        return quant, emb_loss, info

    def encode_to_prequant(self, x):
        h = self.encoder(x)
        h = self.quant_conv(h)
        return h

    def decode(self, quant):
        quant = self.post_quant_conv(quant)
        dec = self.decoder(quant)
        return dec

    def decode_code(self, code_b):
        quant_b = self.quantize.embed_code(code_b)
        dec = self.decode(quant_b)
        return dec

    def forward(self, input, return_pred_indices=False):
        quant, diff, (_, _, ind) = self.encode(input)
        dec = self.decode(quant)
        if return_pred_indices:
            return dec, diff, ind
        return dec, diff

    def get_input(self, batch, k):
        x = batch[k]
        if len(x.shape) == 3:
            x = x[..., None]
        x = x.permute(0, 3, 1,
                      2).to(memory_format=torch.contiguous_format).float()
        if self.batch_resize_range is not None:
            lower_size = self.batch_resize_range[0]
            upper_size = self.batch_resize_range[1]
            if self.global_step <= 4:
                # do the first few batches with max size to avoid later oom
                new_resize = upper_size
            else:
                new_resize = np.random.choice(
                    np.arange(lower_size, upper_size + 16, 16))
            if new_resize != x.shape[2]:
                x = F.interpolate(x, size=new_resize, mode='bicubic')
            x = x.detach()
        return x

    def training_step(self, batch, batch_idx, optimizer_idx):
        # https://github.com/pytorch/pytorch/issues/37142
        # try not to fool the heuristics
        x = self.get_input(batch, self.image_key)
        xrec, qloss, ind = self(x, return_pred_indices=True)

        if optimizer_idx == 0:
            # autoencode
            aeloss, log_dict_ae = self.loss(
                qloss,
                x,
                xrec,
                optimizer_idx,
                self.global_step,
                last_layer=self.get_last_layer(),
                split='train',
                predicted_indices=ind)

            self.log_dict(
                log_dict_ae,
                prog_bar=False,
                logger=True,
                on_step=True,
                on_epoch=True)
            return aeloss

        if optimizer_idx == 1:
            # discriminator
            discloss, log_dict_disc = self.loss(
                qloss,
                x,
                xrec,
                optimizer_idx,
                self.global_step,
                last_layer=self.get_last_layer(),
                split='train')
            self.log_dict(
                log_dict_disc,
                prog_bar=False,
                logger=True,
                on_step=True,
                on_epoch=True)
            return discloss

    def validation_step(self, batch, batch_idx):
        log_dict = self._validation_step(batch, batch_idx)
        # with self.ema_scope():
        #     log_dict_ema = self._validation_step(
        #         batch, batch_idx, suffix='_ema')
        return log_dict

    def _validation_step(self, batch, batch_idx, suffix=''):
        x = self.get_input(batch, self.image_key)
        xrec, qloss, ind = self(x, return_pred_indices=True)
        aeloss, log_dict_ae = self.loss(
            qloss,
            x,
            xrec,
            0,
            self.global_step,
            last_layer=self.get_last_layer(),
            split='val' + suffix,
            predicted_indices=ind)

        discloss, log_dict_disc = self.loss(
            qloss,
            x,
            xrec,
            1,
            self.global_step,
            last_layer=self.get_last_layer(),
            split='val' + suffix,
            predicted_indices=ind)
        rec_loss = log_dict_ae[f'val{suffix}/rec_loss']
        self.log(
            f'val{suffix}/rec_loss',
            rec_loss,
            prog_bar=True,
            logger=True,
            on_step=False,
            on_epoch=True,
            sync_dist=True)
        self.log(
            f'val{suffix}/aeloss',
            aeloss,
            prog_bar=True,
            logger=True,
            on_step=False,
            on_epoch=True,
            sync_dist=True)
        if version.parse(pl.__version__) >= version.parse('1.4.0'):
            del log_dict_ae[f'val{suffix}/rec_loss']
        self.log_dict(log_dict_ae)
        self.log_dict(log_dict_disc)
        return self.log_dict

    def configure_optimizers(self):
        lr_d = self.learning_rate
        lr_g = self.lr_g_factor * self.learning_rate
        print('lr_d', lr_d)
        print('lr_g', lr_g)
        opt_ae = torch.optim.Adam(
            list(self.encoder.parameters()) + list(self.decoder.parameters())
            + list(self.quantize.parameters())
            + list(self.quant_conv.parameters())
            + list(self.post_quant_conv.parameters()),
            lr=lr_g,
            betas=(0.5, 0.9))
        opt_disc = torch.optim.Adam(
            self.loss.discriminator.parameters(), lr=lr_d, betas=(0.5, 0.9))

        if self.scheduler_config is not None:
            scheduler = instantiate_from_config(self.scheduler_config)

            print('Setting up LambdaLR scheduler...')
            scheduler = [
                {
                    'scheduler':
                    LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
                    'interval': 'step',
                    'frequency': 1
                },
                {
                    'scheduler':
                    LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
                    'interval': 'step',
                    'frequency': 1
                },
            ]
            return [opt_ae, opt_disc], scheduler
        return [opt_ae, opt_disc], []

    def get_last_layer(self):
        return self.decoder.conv_out.weight

    def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
        log = dict()
        x = self.get_input(batch, self.image_key)
        x = x.to(self.device)
        if only_inputs:
            log['inputs'] = x
            return log
        xrec, _ = self(x)
        if x.shape[1] > 3:
            # colorize with random projection
            assert xrec.shape[1] > 3
            x = self.to_rgb(x)
            xrec = self.to_rgb(xrec)
        log['inputs'] = x
        log['reconstructions'] = xrec
        if plot_ema:
            with self.ema_scope():
                xrec_ema, _ = self(x)
                if x.shape[1] > 3:
                    xrec_ema = self.to_rgb(xrec_ema)
                log['reconstructions_ema'] = xrec_ema
        return log

    def to_rgb(self, x):
        assert self.image_key == 'segmentation'
        if not hasattr(self, 'colorize'):
            self.register_buffer('colorize',
                                 torch.randn(3, x.shape[1], 1, 1).to(x))
        x = F.conv2d(x, weight=self.colorize)
        x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
        return x


class VQModelInterface(VQModel):

    def __init__(self, embed_dim, *args, **kwargs):
        super().__init__(embed_dim=embed_dim, *args, **kwargs)
        self.embed_dim = embed_dim

    def encode(self, x):
        h = self.encoder(x)
        h = self.quant_conv(h)
        return h

    def decode(self, h, force_not_quantize=False):
        # also go through quantization layer
        if not force_not_quantize:
            quant, emb_loss, info = self.quantize(h)
        else:
            quant = h
        quant = self.post_quant_conv(quant)
        dec = self.decoder(quant)
        return dec


class AutoencoderKL(pl.LightningModule):

    def __init__(
        self,
        ddconfig,
        lossconfig,
        embed_dim,
        ckpt_path=None,
        ignore_keys=[],
        image_key='image',
        colorize_nlabels=None,
        monitor=None,
    ):
        super().__init__()
        self.image_key = image_key
        self.encoder = Encoder(**ddconfig)
        self.decoder = Decoder(**ddconfig)
        self.loss = instantiate_from_config(lossconfig)
        assert ddconfig['double_z']
        self.quant_conv = torch.nn.Conv2d(2 * ddconfig['z_channels'],
                                          2 * embed_dim, 1)
        self.post_quant_conv = torch.nn.Conv2d(embed_dim,
                                               ddconfig['z_channels'], 1)
        self.embed_dim = embed_dim
        if colorize_nlabels is not None:
            assert type(colorize_nlabels) == int
            self.register_buffer('colorize',
                                 torch.randn(3, colorize_nlabels, 1, 1))
        if monitor is not None:
            self.monitor = monitor
        if ckpt_path is not None:
            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)

    def init_from_ckpt(self, path, ignore_keys=list()):
        sd = torch.load(
            path, map_location='cpu', weights_only=True)['state_dict']
        keys = list(sd.keys())
        for k in keys:
            for ik in ignore_keys:
                if k.startswith(ik):
                    print('Deleting key {} from state_dict.'.format(k))
                    del sd[k]
        self.load_state_dict(sd, strict=False)
        print(f'Restored from {path}')

    def encode(self, x):
        h = self.encoder(x)
        moments = self.quant_conv(h)
        posterior = DiagonalGaussianDistribution(moments)
        return posterior

    def decode(self, z):
        z = self.post_quant_conv(z)
        dec = self.decoder(z)
        return dec

    def forward(self, input, sample_posterior=True):
        posterior = self.encode(input)
        if sample_posterior:
            z = posterior.sample()
        else:
            z = posterior.mode()
        dec = self.decode(z)
        return dec, posterior

    def get_input(self, batch, k):
        x = batch[k]
        if len(x.shape) == 3:
            x = x[..., None]
        x = x.permute(0, 3, 1,
                      2).to(memory_format=torch.contiguous_format).float()
        return x

    def training_step(self, batch, batch_idx, optimizer_idx):
        inputs = self.get_input(batch, self.image_key)
        reconstructions, posterior = self(inputs)

        if optimizer_idx == 0:
            # train encoder+decoder+logvar
            aeloss, log_dict_ae = self.loss(
                inputs,
                reconstructions,
                posterior,
                optimizer_idx,
                self.global_step,
                last_layer=self.get_last_layer(),
                split='train')
            self.log(
                'aeloss',
                aeloss,
                prog_bar=True,
                logger=True,
                on_step=True,
                on_epoch=True)
            self.log_dict(
                log_dict_ae,
                prog_bar=False,
                logger=True,
                on_step=True,
                on_epoch=False)
            return aeloss

        if optimizer_idx == 1:
            # train the discriminator
            discloss, log_dict_disc = self.loss(
                inputs,
                reconstructions,
                posterior,
                optimizer_idx,
                self.global_step,
                last_layer=self.get_last_layer(),
                split='train')

            self.log(
                'discloss',
                discloss,
                prog_bar=True,
                logger=True,
                on_step=True,
                on_epoch=True)
            self.log_dict(
                log_dict_disc,
                prog_bar=False,
                logger=True,
                on_step=True,
                on_epoch=False)
            return discloss

    def validation_step(self, batch, batch_idx):
        inputs = self.get_input(batch, self.image_key)
        reconstructions, posterior = self(inputs)
        aeloss, log_dict_ae = self.loss(
            inputs,
            reconstructions,
            posterior,
            0,
            self.global_step,
            last_layer=self.get_last_layer(),
            split='val')

        discloss, log_dict_disc = self.loss(
            inputs,
            reconstructions,
            posterior,
            1,
            self.global_step,
            last_layer=self.get_last_layer(),
            split='val')

        self.log('val/rec_loss', log_dict_ae['val/rec_loss'])
        self.log_dict(log_dict_ae)
        self.log_dict(log_dict_disc)
        return self.log_dict

    def configure_optimizers(self):
        lr = self.learning_rate
        opt_ae = torch.optim.Adam(
            list(self.encoder.parameters()) + list(self.decoder.parameters())
            + list(self.quant_conv.parameters())
            + list(self.post_quant_conv.parameters()),
            lr=lr,
            betas=(0.5, 0.9))
        opt_disc = torch.optim.Adam(
            self.loss.discriminator.parameters(), lr=lr, betas=(0.5, 0.9))
        return [opt_ae, opt_disc], []

    def get_last_layer(self):
        return self.decoder.conv_out.weight

    @torch.no_grad()
    def log_images(self, batch, only_inputs=False, **kwargs):
        log = dict()
        x = self.get_input(batch, self.image_key)
        x = x.to(self.device)
        if not only_inputs:
            xrec, posterior = self(x)
            if x.shape[1] > 3:
                # colorize with random projection
                assert xrec.shape[1] > 3
                x = self.to_rgb(x)
                xrec = self.to_rgb(xrec)
            log['samples'] = self.decode(torch.randn_like(posterior.sample()))
            log['reconstructions'] = xrec
        log['inputs'] = x
        return log

    def to_rgb(self, x):
        assert self.image_key == 'segmentation'
        if not hasattr(self, 'colorize'):
            self.register_buffer('colorize',
                                 torch.randn(3, x.shape[1], 1, 1).to(x))
        x = F.conv2d(x, weight=self.colorize)
        x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
        return x


class IdentityFirstStage(torch.nn.Module):

    def __init__(self, *args, vq_interface=False, **kwargs):
        self.vq_interface = vq_interface  # TODO: Should be true by default but check to not break older stuff
        super().__init__()

    def encode(self, x, *args, **kwargs):
        return x

    def decode(self, x, *args, **kwargs):
        return x

    def quantize(self, x, *args, **kwargs):
        if self.vq_interface:
            return x, None, [None, None, None]
        return x

    def forward(self, x, *args, **kwargs):
        return x
